Epitomic Image Super-Resolution
Authors: Yingzhen Yang, Zhangyang Wang, Zhaowen Wang, Shiyu Chang, Ding Liu, Honghui Shi, Thomas Huang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive objective and subjective evaluation demonstrate the effectiveness and advantage of ESR on various images. We compare our Epitomic Super-Resolution (ESR) to other competing methods in this section, and conduct both objective and subjective evaluation. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 2Adobe Research, San Jose, CA 95110, USA |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code or links to a code repository. |
| Open Datasets | No | The paper mentions evaluating on 'Kid, Temple and Train image' but provides no specific link, DOI, repository name, or formal citation to confirm their public availability or to access them. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the proposed method but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |